Predicting the content of anthraquinone bioactive in Rhei rhizome (Rheum officinale Baill.) with the concentration addition model
Bibliographic record
Abstract
Baill.) (RR) contains a large number of anthraquinone bioactive, yet little is known of the combined effect of these anthraquinones in a mixture. The goals of this study were: to determine the inhibitory potencies of individual anthraquinones and whole RR extract against human liver microsomal CYP1A2/3A4 activity, to predict the content of anthraquinones in RR using the concentration addition (CA) model, and to compare predicted and empirical contents in the same RR sample. Anthraquinone concentrations in the RR extract were determined using HPLC. The inhibitory potencies of individual anthraquinones were determined in incubations containing human liver microsomes. The study results were used to predict an effect-based dose measure of the anthraquinones in RR using the CA model. An empirical dose measure also was determined in the whole RR extract using the CYP1A2/3A4-based bioassay. For the CYP1A2-based studies, the predicted and empirical dose measures of anthraquinones were identical; they were 12.0 ± 1.80 and 12.20 ± 0.81 mg aloe-emodin equivalents/g RR, respectively. For the CYP3A4-based studies, the predicted and empirical dose measures were different; they were 2.80 ± 0.10 and 19.04 ± 0.41 mg aloe-emodin equivalents/g RR, respectively. Only the CYP1A2-based CA model which assumed additive effects of RR anthraquinones predicted an effect-based dose measure that was verifiable by empirical data. The CA model provides an alternative approach to the CYP1A2/3A4-based bioassay or empirical method to screen for the anthraquinones in RR. The CA model as described in this study is applicable to other botanical drugs, plant-based foods and dietary supplements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".